Pre-processed PubMed data for a study of coauthorship

This dataset was collected from the PubMed portal to MEDLINE and other repositories of biomedical research (https://www.ncbi.nlm.nih.gov/pubmed/). Analysis of the dataset led to the paper "Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine", published in PLOS One (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173444). The raw data were pre-processed using the script "clean.r" in the project directory on GitHub (https://github.com/corybrunson/coauthor) to obtain the file presented here.

The dataset is formatted as a data table (https://cran.r-project.org/web/packages/data.table/index.html), a class of data frame in R, and saved as a .RData file, which can be loaded into an R session via `load("path/to/dataset/pmDat.RData")`. The fields are as follows:

• `pmid` - the unique publication identifier (PMID) used by PubMed
• `jid` - the unique journal identifier used by PubMed
• `issn` - the (print) ISSN of the journal
• `ym` - the month and year of publication
• `nau` - the number of authors credited by the publication (up to any limits imposed by PubMed, and counting each author collective as a single author)
• `cau` - whether any corporate author was credited
• `rev` - whether the publication was tagged as a review
• `trial` - whether the publication was tagged as a clinical trial
• `npmt` - the number of MeSH terms assigned to the publication that were flagged as "major" topics
• `nmh` - the number of top-level MeSH headings assigned to the publication
• `supp` - whether the publication was tagged as having received financial support
• `ng` - the number of grants acknowledged by the publication
• `co` - the country in which the journal was published

Note that the field values for any publication can be validated by searching for the PMID in PubMed.